Modeling of intelligent controllers for solar photovoltaic system under varying irradiation conditions

Abstract

The increasing demand for solar renewable energy resources, driven by the global energy crisis and the depletion of conventional energy sources, has underscored the importance of harnessing solar energy. Solar photovoltaic (PV) systems, however, exhibit nonlinear output power due to their weather-dependent nature, impacting overall system efficiency. This study focuses on the development and comparative analysis of three intelligent Maximum Power Point Tracking (MPPT) controllers using the MATLAB Simulink. The controllers employ distinct methodologies, namely, Artificial Neural Networks (ANN), Adaptive Neural and Fuzzy Inference System (ANFIS), and Fuzzy Logic Controller (FLC). The results demonstrate that ANFIS achieved the highest accuracy at 99.50%, surpassing ANN and FLC with accuracies of 97.04% and 98.50%, respectively, thus establishing ANFIS as the superior MPPT controller. Additionally, the positives and negatives of all three MPPT-based algorithms are also compared in this work.